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Artificial Intelligence

Allen School researchers are at the forefront of exciting developments in AI spanning machine learning, computer vision, natural language processing, robotics and more.

We cultivate a deeper understanding of the science and potential impact of rapidly evolving technologies, such as large language models and generative AI, while developing practical tools for their ethical and responsible application in a variety of domains — from biomedical research and disaster response, to autonomous vehicles and urban planning.


Groups & Labs

A hand stacking square blocks in ascending heights like a graph

Interactive Data Lab

The Interactive Data Lab aims to enhance people’s ability to understand and communicate data through the design of new interactive systems for data visualization and analysis.

Human-Centered Robotics Lab photo of a robot assisting with picking up a bottle

Human-Centered Robotics Lab

In the Human-Centered Robotics lab we aim to develop robotics that are useful and usable for future users of task-oriented robots.


Faculty Members

Faculty

Faculty


Centers & Initiatives

The Tech Policy Lab is a unique, interdisciplinary collaboration at the University of Washington that aims to enhance technology policy through research, education, and thought leadership. Founded in 2013 by faculty from the Paul G. Allen School of Computer Science & Engineering, Information School, and School of Law, the Lab aims to bridge the gap between technologists and policymakers and to help generate wiser, more inclusive tech policy.

The Science Hub supports a broad set of programs — including fellowships for doctoral students, collaboration among researchers and support for collaborative research events — designed to accelerate artificial intelligence (AI), robotics and engineering in the Seattle area.

Highlights


Allen School News

A team of Allen School and Ai2 researchers were recognized for developing an efficient, scalable system for indexing petabyte-level text corpora with minimal storage overhead to better understand the data on which large language models are trained.

Allen School News

Allen School researchers led the development of a benchmark dataset of 26,000 real-world, open-ended queries to evaluate the creative generation of large language models. They discovered major LLMs all generate similar outputs as if they’re part of an Artificial Hivemind.

Business Insider

Farhadi, who co-leads the Allen School’s Reasoning, AI, and VisioN (RAIVN) Lab and is also CEO of the Allen Institute for Artificial Intelligence (Ai2), was recognized for his leadership in open AI research and his influence on how institutions scale AI for the benefit of humanity.